Customer assistants
Chat and voice assistants that answer from your docs, orders and policies, and hand over to a person when they should.
For example: a support bot that resolves order-status questions and books callbacks.
Put AI to work in your product and your operations.
We design, build and run AI that is useful from day one: assistants that know your business, agents that take over repetitive work, and AI that watches your infrastructure. Private by default and measured before launch.
Works withOpenAIAnthropic ClaudeGoogle GeminiOpen-source models
Incident assistant
Why did checkout errors jump at 14:05?
Errors on checkout-api rose from 0.2% to 6.8% two minutes after release 3f9c2a1. Most failures are timeouts calling payments-service, because the release lowered its HTTP timeout from 10s to 2s. Rolling back, or restoring the timeout, should clear it.
checkout-api logsrelease 3f9c2a1payments latency
Roll back releaseOpen incident
What we build with AI
Start with one problem that costs you time or money. We build the smallest thing that fixes it, then grow it.
Knowledge search
Ask questions across Drive, Confluence, Notion and past tickets. Every answer links to the source it came from.
For example: an internal assistant for policies, SOPs and runbooks.
AI agents and automation
Agents that read emails and documents, fill forms, update your CRM and raise tickets, with approval steps wherever money or data is involved.
For example: invoice intake that extracts details and drafts entries for review.
AI features in your apps
Summaries, smart search, recommendations and document or image understanding, added to the web and mobile apps you already have.
For example: one-click summaries of long customer conversations.
AIOps for infrastructure
Log and error analysis, alert grouping and incident summaries connected to your monitoring, so on-call engineers start with an answer.
For example: an assistant that explains error spikes after a deploy.
Private and self-hosted AI
Open-source models running in your own cloud or Kubernetes cluster, for data that cannot leave your environment.
For example: a private model on GPU nodes, reachable only inside your network.
From idea to production, without the hype
Most AI projects stall between the demo and real users. Our process is built around that gap.
Find the use case
We look at where time or money is lost today and pick the problem worth solving first.
Prototype on your data
A working prototype using your real documents and systems, not a generic demo.
Measure
We test answers against real questions and track accuracy, speed and cost before launch.
Launch with guardrails
Permissions, logging and human review where needed, released gradually to real users.
Improve
We monitor quality and cost in production and update prompts and data as your business changes.
Built to be trusted
AI that touches your customers and your data has to be safe, predictable and affordable.
Your data stays yours
We use providers and settings that do not train on your data, or run models inside your own cloud.
Measured, not guessed
Every release is tested against a set of real questions, so you know how well it works before users do.
Guardrails built in
Role-based access, personal data redaction and approval steps before any action that costs money.
Costs under control
Right-sized models, caching and usage budgets, with a dashboard showing what each feature costs to run.
The AI stack we work with
We are not tied to one provider. We choose models on quality, cost and where your data is allowed to go.
Models
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
Frameworks
- LangChain
- LlamaIndex
- Vercel AI SDK
- FastAPI
Data and search
- PostgreSQL with pgvector
- Qdrant
- Pinecone
- Elasticsearch
Deployment
- AWS Bedrock
- Azure OpenAI
- vLLM and Ollama
- Kubernetes
Monitoring
- Langfuse
- OpenTelemetry
- Prometheus
- Grafana
Questions about AI projects
Will our data be used to train AI models?
No. We use provider settings and business plans that exclude your data from training, and for sensitive data we can run open-source models inside your own cloud.
Can the AI run inside our own cloud?
Yes. We deploy models on your AWS, Azure or Kubernetes environment, or use managed services such as AWS Bedrock and Azure OpenAI in your account and region.
How long until we see something working?
A first prototype on your real data usually takes 3–4 weeks. We then measure it with you before deciding what to launch.
How much does an AI project cost?
Prototypes start from ₹1,00,000. Running costs depend on usage and model choice, and we give you an estimate before anything goes live.
Do we need our own data scientists?
No. We handle the build and the running of it, and train your team to manage content and review quality themselves.
Have a process AI could take off your plate?
Tell us where the time goes today. We will tell you honestly whether AI is the right fix, and what it would take.